 ;; @@
 (def torch-connection (start-torch-connection gaussian [1] combine-observes-fn))
 ;; @@
+;; =>
+;;; {"type":"html","content":"<span class='clj-var'>#&#x27;worksheets.gaussian/torch-connection</span>","value":"#'worksheets.gaussian/torch-connection"}
+;; <=
 
 ;; **
 ;;; Then run the following to train the neural network:
-;;;
+;;; 
 ;;; `python -m infcomp.compile`
 ;; **
 
 ;; @@
 (stop-torch-connection torch-connection)
 ;; @@
+;; =>
+;;; {"type":"html","content":"<span class='clj-string'>&quot;Torch connection terminated.&quot;</span>","value":"\"Torch connection terminated.\""}
+;; <=
 
 ;; **
 ;;; ## Inference

;; @@
(ns worksheets.gaussian
  (:require [anglican.runtime :refer :all]
            [anglican.emit :refer [defquery]]
            [anglican.stat :refer [empirical-distribution collect-results]]
            anglican.infcomp.csis
            anglican.importance
            [anglican.infcomp.network :refer :all]
            [anglican.inference :refer [infer]])
  (:use [gorilla-plot core]))
;; @@

;; **
;;; Define a Gaussian unknown mean model


;; @@
(defquery gaussian [obs]
  (let [mean (sample (normal 0 1))
        std 0.1]
    (observe (normal mean std) obs)
    mean))
;; @@
;; =>
;;; {"type":"html","content":"<span class='clj-var'>#&#x27;worksheets.gaussian/gaussian</span>","value":"#'worksheets.gaussian/gaussian"}
;; <=

;; **
;;; ## Inference Compilation
